Biography

I am pursuing my master degree in College of Control Engineering, Zhejiang University, Hangzhou, China. My major research interest is Reinforcement learning, Multi-agent cooperation and Path Planning.

Research and Interests

• Multi-agent cooperation
• Path Planning
• Reinforcement learning

Publications

• Gang Xu, Yuchen Wu, Sheng Tao, Yifan Yang, Tao Liu, Tao Huang, Huifeng Wu, and Yong Liu. Efficient Multi-Robot Task and Path Planning in Large-Scale Cluttered Environments. IEEE Robotics and Automation Letters, 10:9112-9119, 2025.
  [BibTeX] [Abstract] [DOI] [PDF]

  As the potential of multi-robot systems continues to be explored and validated across various real-world applications, such as package delivery, search and rescue, and autonomous exploration, the need to improve the efficiency and quality of task and path planning has become increasingly urgent, particularly in large-scale, obstacle-rich environments. To this end, this letter investigates the problem of multi-robot task and path planning (MRTPP) in large-scale cluttered scenarios. Specifically, we first propose an obstacle-vertex search (OVS) path planner that quickly constructs the cost matrix of collision-free paths for multi-robot task planning, ensuring the rationality of task planning in obstacle-rich environments. Furthermore, we introduce an efficient auction-based method for solving the MRTPP problem by incorporating a novel memory-aware strategy, aiming to minimize the maximum travel cost among robots for task visits. The proposed method effectively improves computational efficiency while maintaining solution quality in the multi-robot task planning problem. Finally, we demonstrated the effectiveness and practicality of the proposed method through extensive benchmark comparisons.

  @article{xu2025emr,
  title = {Efficient Multi-Robot Task and Path Planning in Large-Scale Cluttered Environments},
  author = {Gang Xu and Yuchen Wu and Sheng Tao and Yifan Yang and Tao Liu and Tao Huang and Huifeng Wu and Yong Liu},
  year = 2025,
  journal = {IEEE Robotics and Automation Letters},
  volume = 10,
  pages = {9112-9119},
  doi = {10.1109/LRA.2025.3592146},
  abstract = {As the potential of multi-robot systems continues to be explored and validated across various real-world applications, such as package delivery, search and rescue, and autonomous exploration, the need to improve the efficiency and quality of task and path planning has become increasingly urgent, particularly in large-scale, obstacle-rich environments. To this end, this letter investigates the problem of multi-robot task and path planning (MRTPP) in large-scale cluttered scenarios. Specifically, we first propose an obstacle-vertex search (OVS) path planner that quickly constructs the cost matrix of collision-free paths for multi-robot task planning, ensuring the rationality of task planning in obstacle-rich environments. Furthermore, we introduce an efficient auction-based method for solving the MRTPP problem by incorporating a novel memory-aware strategy, aiming to minimize the maximum travel cost among robots for task visits. The proposed method effectively improves computational efficiency while maintaining solution quality in the multi-robot task planning problem. Finally, we demonstrated the effectiveness and practicality of the proposed method through extensive benchmark comparisons.}
  }

• Yifan Yang, Yuchen Wu, Gang Xu, Yong Liu, Zhitao Zhang, and Jian Yang. Intelligent Hybrid Decision-Making for High-Speed Autonomous Driving Scenarios. In The 25th COTA International Conference of Transportation Professionals (CICTP), 2025.
  [BibTeX]

  @inproceedings{yang2025ihd,
  title = {Intelligent Hybrid Decision-Making for High-Speed Autonomous Driving Scenarios},
  author = {Yifan Yang and Yuchen Wu and Gang Xu and Yong Liu and Zhitao Zhang and Jian Yang},
  year = 2025,
  booktitle = {The 25th COTA International Conference of Transportation Professionals (CICTP)}
  }

• Yuchen Wu, Yifan Yang, Gang Xu, Junjie Cao, Yansong Chen, Licheng Wen, and Yong Liu. Hierarchical Search-Based Cooperative Motion Planning. In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 8055-8062, 2024.
  [BibTeX] [Abstract] [DOI] [PDF]

  Cooperative path planning, a crucial aspect of multi-agent systems research, serves a variety of sectors, including military, agriculture, and industry. Many existing algorithms, however, come with certain limitations, such as simplified kinematic models and inadequate support for multiple group scenarios. Focusing on the planning problem associated with a nonholonomic Ackermann model for Unmanned Ground Vehicles (UGV), we propose a leaderless, hierarchical Search-Based Cooperative Motion Planning (SCMP) method. The high-level utilizes a binary conflict search tree to minimize runtime, while the low-level fabricates kinematically feasible, collision-free paths that are shape-constrained. Our algorithm can adapt to scenarios featuring multiple groups with different shapes, outlier agents, and elaborate obstacles. We conduct algorithm comparisons, performance testing, simulation, and real-world testing, verifying the effectiveness and applicability of our algorithm. The implementation of our method will be open-sourced at https://github.com/WYCUniverStar/SCMP.

  @inproceedings{wu2024hsb,
  title = {Hierarchical Search-Based Cooperative Motion Planning},
  author = {Yuchen Wu and Yifan Yang and Gang Xu and Junjie Cao and Yansong Chen and Licheng Wen and Yong Liu},
  year = 2024,
  booktitle = {2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  pages = {8055-8062},
  doi = {10.1109/IROS58592.2024.10801442},
  abstract = {Cooperative path planning, a crucial aspect of multi-agent systems research, serves a variety of sectors, including military, agriculture, and industry. Many existing algorithms, however, come with certain limitations, such as simplified kinematic models and inadequate support for multiple group scenarios. Focusing on the planning problem associated with a nonholonomic Ackermann model for Unmanned Ground Vehicles (UGV), we propose a leaderless, hierarchical Search-Based Cooperative Motion Planning (SCMP) method. The high-level utilizes a binary conflict search tree to minimize runtime, while the low-level fabricates kinematically feasible, collision-free paths that are shape-constrained. Our algorithm can adapt to scenarios featuring multiple groups with different shapes, outlier agents, and elaborate obstacles. We conduct algorithm comparisons, performance testing, simulation, and real-world testing, verifying the effectiveness and applicability of our algorithm. The implementation of our method will be open-sourced at https://github.com/WYCUniverStar/SCMP.}
  }

• Helei Yang, Peng Ge, Junjie Cao, Yifan Yang, and Yong Liu. Large Scale Pursuit-Evasion Under Collision Avoidance Using Deep Reinforcement Learning. In 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 2232-2239, 2023.
  [BibTeX] [Abstract] [DOI] [PDF]

  This paper examines a pursuit-evasion game (PEG) involving multiple pursuers and evaders. The decentralized pursuers aim to collaborate to capture the faster evaders while avoiding collisions. The policies of all agents are learning-based and are subjected to kinematic constraints that are specific to unicycles. To address the challenge of high dimensionality encountered in large-scale scenarios, we propose a state processing method named Mix-Attention, which is based on Self-Attention. This method effectively mitigates the curse of dimensionality. The simulation results provided in this study demonstrate that the combination of Mix-Attention and Independent Proximal Policy Optimization (IPPO) surpasses alternative approaches when solving the multi-pursuer multi-evader PEG, particularly as the number of entities increases. Moreover, the trained policies showcase their ability to adapt to scenarios involving varying numbers of agents and obstacles without requiring retraining. This adaptability showcases their transferability and robustness. Finally, our proposed approach has been validated through physical experiments conducted with six robots.

  @inproceedings{yang2023lsp,
  title = {Large Scale Pursuit-Evasion Under Collision Avoidance Using Deep Reinforcement Learning},
  author = {Helei Yang and Peng Ge and Junjie Cao and Yifan Yang and Yong Liu},
  year = 2023,
  booktitle = {2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  pages = {2232-2239},
  doi = {10.1109/IROS55552.2023.10341975},
  abstract = {This paper examines a pursuit-evasion game (PEG) involving multiple pursuers and evaders. The decentralized pursuers aim to collaborate to capture the faster evaders while avoiding collisions. The policies of all agents are learning-based and are subjected to kinematic constraints that are specific to unicycles. To address the challenge of high dimensionality encountered in large-scale scenarios, we propose a state processing method named Mix-Attention, which is based on Self-Attention. This method effectively mitigates the curse of dimensionality. The simulation results provided in this study demonstrate that the combination of Mix-Attention and Independent Proximal Policy Optimization (IPPO) surpasses alternative approaches when solving the multi-pursuer multi-evader PEG, particularly as the number of entities increases. Moreover, the trained policies showcase their ability to adapt to scenarios involving varying numbers of agents and obstacles without requiring retraining. This adaptability showcases their transferability and robustness. Finally, our proposed approach has been validated through physical experiments conducted with six robots.}
  }